Applied Algorithms for Bioinformatics Problems

Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research

This work presents analytical solutions and numerical algorithms for advanced elastic contact with friction engineering problems found in such dynamical examples as vehicle collisions or robot control

Although topology was recognized by Gauss and Maxwell to play a pivotal role in the formulation of electromagnetic boundary value problems, it is a largely unexploited tool for field computation. The development of algebraic topology since Maxwell provides a framework for linking data structures, algorithms, and computation to topological aspects of three-dimensional electromagnetic boundary value problems. This book attempts to expose the link between Maxwell and a modern approach to algorithms. The first chapters lay out the relevant facts about homology and cohomology, stressing their interpretations in electromagnetism. These topological structures are subsequently tied to variational formulations in electromagnetics, the finite element method, algorithms, and certain aspects of numerical linear algebra. A recurring theme is the formulation of and algorithms for the problem of making branch cuts for computing magnetic scalar potentials and eddy currents.

Bioinformatics, Biocomputing and Perl presents a modern introduction to bioinformatics computing skills and practice. Structuring its presentation around four main areas of study, this book covers the

A companion volume to Beginning Perl for Bioinformatics discusses the ties between biology and computer science and covers core PERL programming concepts, module extensions, and more advanced Perl tec

DNA microarrays have revolutionised molecular biology and are becoming a standard tool in the field. Written for those from both laboratory and bioinformatics backgrounds, this book is a comprehensive

Once again, Robert Sedgewick provides a current and comprehensive introduction to important algorithms. The focus this time is on graph algorithms, which are increasingly critical for a wide range of

In this supplementary undergraduate text, Steeb (applied mathematics, Rand Afrikaans University) walks through the solution of 154 problems for engineering students learning how to work with the discr

Parallel Algorithms for Regular Architectures is the first book to concentrate exclusively on algorithms and paradigms for programming parallel computers such as the hypercube, mesh, pyramid, and mesh

Applied kinesiology uses diagnostic muscle tests in conjunction with standard examination procedures to determine the causes of health problems. Written for professionals and nonprofessionals alike, A

Outstanding text for graduate students and researchers proposes improvements to existing algorithms, extends their related mathematical theories, and offers details on new algorithms for approximating

Once again, Robert Sedgewick provides a current and comprehensive introduction to important algorithms. The focus this time is on graph algorithms, which are increasingly critical for a wide range of

Applied Ethics focuses the central concepts of traditional morality - rights, justice, the good, virtue, and the fundamental value of human life - on a number of pressing contemporary problems, includ

Various problems in computer science are 'hard', that is NP-complete, and so not realistically computable; thus in order to solve them they have to be approximated. This book is a survey of the basic techniques for approximating combinatorial problems using parallel algorithms. Its core is a collection of techniques that can be used to provide parallel approximations for a wide range of problems (for example, flows, coverings, matchings, travelling salesman problems, graphs), but in order to make the book reasonably self-contained, the authors provide an introductory chapter containing the basic definitions and results. A final chapter deals with problems that cannot be approximated, and the book is ended by an appendix that gives a convenient summary of the problems described in the book. This is an up-to-date reference for research workers in the area of algorithms, but it can also be used for graduate courses in the subject.

Parallel-Algorithms for Regular Architectures is the first book to concentrate exclusively on algorithms and paradigms for programming parallel computers such as the hypercube, mesh, pyramid, and mesh

For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.

This hypermedia CD-ROM provides an ideal format for the visual explanation of complex algorithms contained in the text Introduction to Algorithms, by Thomas H. Cormen, Charles E. Leiserson, and Ronal

First published in 1993, this thesis is concerned with the design of efficient algorithms for listing combinatorial structures. The research described here gives some answers to the following questions: which families of combinatorial structures have fast computer algorithms for listing their members? What general methods are useful for listing combinatorial structures? How can these be applied to those families which are of interest to theoretical computer scientists and combinatorialists? Amongst those families considered are unlabelled graphs, first order one properties, Hamiltonian graphs, graphs with cliques of specified order, and k-colourable graphs. Some related work is also included, which compares the listing problem with the difficulty of solving the existence problem, the construction problem, the random sampling problem, and the counting problem. In particular, the difficulty of evaluating Pólya's cycle polynomial is demonstrated.

Emphasizes electromagnetic and microwave problems and the fundamental algorithms which can be used as the basis for computer programs that produce useful numerical results. Includes relevant computer

By the time students have done some programming in one or two languages and have learnt the common ways of representing information in a computer, they will want to embark upon further study of theoretical or applied topics in computer science. Most will encounter problems that require for their solution one or more of the techniques described in this book: for example problems depending upon the formation and solution of different equations; the task of making lists of possible alternatives and of answering questions about them; or the search for discrete optima. Written by the same authors as the highly successful Information Representation and Manipulation in a Computer, this book describes algorithms of mathematical methods and illustrates their application with examples. The mathematical background needed is elementary algebra and calculus. Numerous exercises are provided, with hints to their solutions.

In a world increasingly run by algorithms and artificial intelligence, Hatim Rahman traces how organizations are using algorithms to control workers in an "invisible cage." Inside the Invisible Cage uses unique longitudinal data to investigate how digital labor platforms use algorithms to dictate the actions of high-skilled workers by determining accepted behaviors, work opportunities, and even success. As Hatim Rahman explains, employers can use algorithms to shift rules and guidelines without notice, explanation, or recourse for workers. The invisible cage signals a profound shift in the way markets and organizations categorize and ultimately control people. Unlike previous forms of labor control, the invisible cage is ubiquitous, yet it is also opaque and shifting, which makes breaking free from it difficult for workers. This book traces how the invisible cage was developed over time and the implications it has for the spread of new technology, such as generative artificial intellig

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves as both a starting point for researchers and graduate students in mathematics trying to get into the field, as well as an invaluable reference for future research.

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

MATLAB examples found throughout the bookNumerical simulation helps students understand theory•MATLAB 2.3 shows when connecting wire must be treated as transmission line. The student can see WHY the T-Line model is required, rather than just accepting some minimum length criteria on faith.•In MATLAB example 8.4, a movie is created showing the radiation pattern from a dipole antenna as its length changes.Many homework problems draw on the MATLAB examplesSimulations can point out when ideal theory can be applied to actual situations•In MATLAB problem 2.18, students are asked to compare the field from a segment of charge to the field from an infinite length line of charge. The problem is similar to MATLAB example 2.3, so the programming is not difficult. Students are able to see that theory for an unrealistic infinite line can be put into practice in some situations.Example Problems•Students see the detailed steps needed to solve typical problemsDrill Problems•Simple problems reinforce pr

Using easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses,

Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-sem

String matching is one of the oldest algorithmic techniques, yet still one of the most pervasive in computer science. The past 20 years have seen technological leaps in applications as diverse as information retrieval and compression. This copiously illustrated collection of puzzles and exercises in key areas of text algorithms and combinatorics on words offers graduate students and researchers a pleasant and direct way to learn and practice with advanced concepts. The problems are drawn from a large range of scientific publications, both classic and new. Building up from the basics, the book goes on to showcase problems in combinatorics on words (including Fibonacci or Thue-Morse words), pattern matching (including Knuth-Morris-Pratt and Boyer-Moore like algorithms), efficient text data structures (including suffix trees and suffix arrays), regularities in words (including periods and runs) and text compression (including Huffman, Lempel-Ziv and Burrows-Wheeler based methods).

String matching is one of the oldest algorithmic techniques, yet still one of the most pervasive in computer science. The past 20 years have seen technological leaps in applications as diverse as information retrieval and compression. This copiously illustrated collection of puzzles and exercises in key areas of text algorithms and combinatorics on words offers graduate students and researchers a pleasant and direct way to learn and practice with advanced concepts. The problems are drawn from a large range of scientific publications, both classic and new. Building up from the basics, the book goes on to showcase problems in combinatorics on words (including Fibonacci or Thue-Morse words), pattern matching (including Knuth-Morris-Pratt and Boyer-Moore like algorithms), efficient text data structures (including suffix trees and suffix arrays), regularities in words (including periods and runs) and text compression (including Huffman, Lempel-Ziv and Burrows-Wheeler based methods).

Neonatal hematology is a fast-growing field, and the majority of sick neonates will develop hematological problems. This is an essential guide to the pathogenesis, diagnosis and management of hematologic problems in the neonate. Guidance is practical, including blood test interpretation, advice on transfusions and reference ranges for hematological values. Chapters have been thoroughly revised according to the latest advances in the field for this updated third edition. Topics discussed include erythrocyte disorders, platelet disorders, leukocyte disorders, immunologic disorders and hemostatic disorders. Coverage of oncological issues has been expanded to two separate chapters on leukemia and solid tumors, making information more easily accessible. Approaches to identifying the cause of anemia in a neonate are explained, with detailed algorithms provided to aid clinicians in practice. Covering an important hematologic niche with an ever increasing amount of specialized knowledge, this

Dive Into Algorithms is a broad introduction to algorithms using the Python Programming Language.Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithm

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Organizations and businesses strive toward excellence, and solutions to problems are based mostly on judgment and experience. However, increased competition and consumer demands require that the solutions be optimum and not just feasible. Theory leads to algorithms. Algorithms need to be translated into computer codes. Engineering problems need to be modeled. Optimum solutions are obtained using theory and computers, and then interpreted. Revised and expanded in its third edition, this textbook integrates theory, modeling, development of numerical methods, and problem solving, thus preparing students to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multi-objective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element-based optimization. It is ideal for advanced undergraduate or graduate cours

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

This book presents and applies a framework for studying the complexity of algorithms. It is aimed at logicians, computer scientists, mathematicians and philosophers interested in the theory of computation and its foundations, and it is written at a level suitable for non-specialists. Part I provides an accessible introduction to abstract recursion theory and its connection with computability and complexity. This part is suitable for use as a textbook for an advanced undergraduate or graduate course: all the necessary elementary facts from logic, recursion theory, arithmetic and algebra are included. Part II develops and applies an extension of the homomorphism method due jointly to the author and Lou van den Dries for deriving lower complexity bounds for problems in number theory and algebra which (provably or plausibly) restrict all elementary algorithms from specified primitives. The book includes over 250 problems, from simple checks of the reader's understanding, to current open pr

This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and process

A pioneering monograph on tensor methods applied to distributional problems arising in statistics, this work constitutes a valuable reference for graduate students and professional statisticians. Prer

Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that

A hands-on approach to historical linguistics, working through 101 problems in five different categories.This book offers a hands-on approach to historical linguistics, guiding the student through 101 problems in five different categories. These include 12 problems on the establishment of genetic relationship among languages, 24 problems on sound change, 35 problems on phonological reconstruction, 10 problems on internal reconstruction, and 20 problems on subgrouping. Each section begins with an introduction to the principles of historical linguistics as applied to the topic at hand. The problems come next, ordered by level of difficulty -- beginning, intermediate, advanced. The "Solutions" section at the end of the book provides answers.The book provides a consistent structure for each section, offering an overview of the topic followed by progressively difficult problems. Examples come from a wide range of languages, including Austronesian languages. The book provides explicit soluti